CloSeR: Unified Relational Distillation from Closed-Set Teachers for Category Discovery

Generalized Category Discovery (GCD) is an intriguing open-world problem that has garnered increasing attention: given partially la-belled data, the goal is to correctly recognize known classes while discov-ering coherent novel categories from unlabelled samples. Recent GCDmethods typically adapt foundation models by jointly optimizing su-pervised classification and unsupervised discovery objectives on mixedlabelled–unlabelled data. While effective, this coupled training can en-tangle closed-set recognition and open-set discovery, leading to objectiveconflict and biased predictions, and may disturb the semantic geome-try of pretrained representations under limited labels and noisy pseudo-labels. We propose CloSeR, a simple plug-and-play framework that in-jects Closed-Set Relational knowledge into GCD training. CloSeR firstbuilds a domain-adapted closed-set teacher by tuning lightweight block-wise adapters on labelled known-class data while keeping the founda-tion model backbone frozen, thereby preserving pretrained priors at lowtraining cost. It then transfers the teacher’s knowledge to downstreamGCD via Unified Relational Distillation (URD), which distills comple-mentary global sample-to-prototype relations to anchor known-class se-mantics and local sample-to-sample relations to preserve neighborhoodstructure, using separate feature pathways to reduce optimization in-terference. CloSeR is head-agnostic and readily integrates with bothparametric and non-parametric GCD methods. Extensive experimentswith DINO and DINOv2 backbones on six benchmarks (CIFAR-10/100,ImageNet-100, CUB, Stanford-Cars, and FGVC-Aircraft) show consis-tent gains over GCD baselines, achieving state-of-the-art performance.Project page: https://visual-ai.github.io/closer/